Metkagram · method
The sentence stays readable. Its structure becomes inspectable.
Metkagram is a research-oriented approach to language learning. It combines compact functional annotation in natural sentences, applied work from cognitive learning research, and NLP-ready data so grammar becomes observable evidence rather than an abstract rule.
I want to develop more effective study habits.
Can you help?
Have you tried spaced repetition?
01 · The complete learning loop
Sentence → Signal → Structure → Pattern → Variation → Recall
- 01Sentence
Read one complete, meaningful sentence.
- 02Signal
Notice a minimal tag beside a word or span.
- 03Structure
Connect the tag to the exact part it describes.
- 04Pattern
Identify a reusable chunk or structural pattern.
- 05Variation
Compare it across pronouns, questions, negatives, tenses and contexts.
- 06Recall
Attempt to retrieve it before the answer is shown.
- 07Spaced review
Return to it later through spaced review.
02 · Before and after annotation
Meaning first, cue second
I want to develop more effective study habits.
I want to develop more effective study habits.
Try to retrieve the pattern before revealing the answer.03 · B2–C1 variation
One structure, new situations
If + Past Simple, would + V
If I had more time, I would start a side project.
Wenn ich mehr Zeit hätte, würde ich ein Nebenprojekt starten.
Questions, negatives, people and tenses change the sentence while making the reusable structure visible.04 · A research system, not a collection of labels
A research system, not a collection of labels
Metkagram develops its own applied annotation scheme; it does not claim to have invented linguistic annotation, colour coding, retrieval practice or spaced repetition. We integrate token-level functional tags with natural sentences, explanations, translations, formulas, systematic variation, active recall and spacing. The result is a publicly inspectable structured corpus that serves both learning and NLP analysis: word roles can be traced, constructions compared, and reproducible experiments built on the same evidence.
A publicly inspectable, machine-readable corpus with token-level functional annotation
05 · What informs the design
Research informs the logic; it does not promise the outcome.
A selective visual cue can support attention to one functionally relevant detail without asking the learner to parse every part at once.
Inline annotation in meaningful input is consistent with focus on form: the form remains inside a sentence with meaning.
A token-level annotation scheme makes the learning markup readable to people and ready for computational analysis.
A short tag beside its word is designed to reduce split attention between a rule screen and the sentence.
A sentence and its reusable chunk can support contextual encoding and later transfer to a new situation.
Attempting retrieval before feedback can support later access to a pattern.
Spaced return and systematic variation help test what changes and what remains reusable.
06 · Limits
Limits
A research-oriented, NLP-ready foundation strengthens the method; it does not turn it into an automatic language-learning machine. These mechanisms inform the design. Tags do not replace reading, conversation, feedback, vocabulary work or practice.
07 · Verified sources
Verified sources
Titles, authors, years and links distinguish research findings from Metkagram’s design interpretation.